-That last model sucked. Let’s try again with a better model.
##
## Family: gaussian
## Link function: identity
##
## Formula:
## NetWins ~ normIPouts + normBB + normSO + normBAOpp + normERA
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0002319 0.0158333 0.015 0.9883
## normIPouts 1.3883292 0.0431626 32.165 < 2e-16 ***
## normBB -1.8568112 0.0353850 -52.475 < 2e-16 ***
## normSO 1.2529491 0.0316495 39.588 < 2e-16 ***
## normBAOpp -0.0398335 0.0159113 -2.503 0.0123 *
## normERA -0.1265619 0.0163386 -7.746 9.68e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## R-sq.(adj) = 0.139 Deviance explained = 13.9%
## GCV = 10.81 Scale est. = 10.809 n = 43116
## NULL
##Building a Generalized Linear Model
-That last model sucked too. Let’s try another one–a generalized linear model.
##
## Call:
## glm(formula = NetWins ~ normIPouts + normBB + normSO + normBAOpp +
## normERA, family = gaussian, data = pitchers)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -22.0234 -1.4264 0.3797 1.3818 22.3251
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0002319 0.0158333 0.015 0.9883
## normIPouts 1.3883292 0.0431626 32.165 < 2e-16 ***
## normBB -1.8568112 0.0353850 -52.475 < 2e-16 ***
## normSO 1.2529491 0.0316495 39.588 < 2e-16 ***
## normBAOpp -0.0398335 0.0159113 -2.503 0.0123 *
## normERA -0.1265619 0.0163386 -7.746 9.68e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 10.80891)
##
## Null deviance: 541146 on 43115 degrees of freedom
## Residual deviance: 465972 on 43110 degrees of freedom
## AIC: 224998
##
## Number of Fisher Scoring iterations: 2
## NULL